For the first toy example, we use the Fashion MNIST data set [1]} which is similar to MNIST.
Both data sets contain the same number of training and test images, share a common image size and are grayscale.
Fashion MNIST, in contrast, contains images of 10 different clothings or accessories which are more difficult object categories than handwritten digits.
In the test data, we introduce a systematic error by changing
the target class of 90% of the examples of class “0” (“T-shirt/Top”) to class “1” (“trouser”).
Using this altered test data, we create topographic maps for a MLP model that is trained on the original Fashion MNIST training data.
In the second toy example, we use the MNIST data set and introduce a systematic error in its training data.
Specifically, we change 90% of the class “0” examples to class “1” and train a MLP model on this altered training data set.
For this model, we create topographic maps for the original MNIST test data.
Both toy examples only use the shallow MLP architecture as described in Section REF  and we use an unrealistically high amount of mislabeled examples to facilitate the understanding of the visualization.
Nevertheless, both examples represent real-world scenarios that we discuss in the corresponding sections.
